

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = None     ,				):
							A      :					Any      		=					{}

							if train_file is not None:
														A      :					Any      		=					[train_file]
							if eval_file is not None:
														A      :					Any      		=					[eval_file]
							if test_file is not None:
														A      :					str      		=					[test_file]

							A      :					int      		=					datasets.load_dataset('''csv'''     ,				data_files=lowerCamelCase_		)
							A      :					Union[str, Any]      		=					list(ds[list(files.keys()		)[0]].features.keys()		)
							A      :					Optional[int]      		=					features_name.pop(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					list(set(ds[list(files.keys()		)[0]][label_name]		)		)
							A      :					Any      		=					{label: i for i, label in enumerate(lowerCamelCase_		)}
							A      :					Optional[Any]      		=					tokenizer.model_input_names
							A      :					Optional[Any]      		=					{}

							if len(lowerCamelCase_		) == 1:
														for k in files.keys():
																					A      :					List[Any]      		=					ds[k].map(
																					    lambda lowerCamelCase_		: tokenizer.batch_encode_plus(
																					        example[features_name[0]]     ,				truncation=lowerCamelCase_     ,				max_length=lowerCamelCase_     ,				padding='''max_length'''		)     ,				batched=lowerCamelCase_     ,				)
							elif len(lowerCamelCase_		) == 2:
														for k in files.keys():
																					A      :					Tuple      		=					ds[k].map(
																					    lambda lowerCamelCase_		: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])     ,				truncation=lowerCamelCase_     ,				max_length=lowerCamelCase_     ,				padding='''max_length'''     ,				)     ,				batched=lowerCamelCase_     ,				)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					A      :					Any      		=					{k: v for k, v in ex.items() if k in input_names}
																					A      :					List[Any]      		=					labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					A      :					Dict      		=					{k: v for k, v in ex.items() if k in input_names}
																					A      :					Dict      		=					labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					A      :					List[str]      		=					{k: v for k, v in ex.items() if k in input_names}
																					A      :					int      		=					labelaid[ex[label_name]]
																					yield (d, label)

							A      :					Optional[Any]      		=					(
							    tf.data.Dataset.from_generator(
							        lowerCamelCase_     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))     ,				)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														A      :					int      		=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

							A      :					str      		=					(
							    tf.data.Dataset.from_generator(
							        lowerCamelCase_     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))     ,				)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														A      :					List[str]      		=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

							A      :					Optional[int]      		=					(
							    tf.data.Dataset.from_generator(
							        lowerCamelCase_     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))     ,				)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														A      :					Union[str, Any]      		=					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

							return train_ds, val_ds, test_ds, labelaid


lowercase			:				int   =     logging.getLogger(__name__)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	field(metadata={'''help''': '''Which column contains the label'''}				)
							UpperCAmelCase_						:		str								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The path of the training file'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The path of the development file'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The path of the test file'''}				)
							UpperCAmelCase_						:		int								=	field(
							    default=1_28		,       metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Set this flag to use fast tokenization.'''}				)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							UpperCAmelCase_						:		Optional[str]								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )






def        snake_case__		(    ):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.
							A      :					Union[str, Any]      		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
							A		,		A		,		A      :					Dict      		=					parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir		)
							    and os.listdir(training_args.output_dir		)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    ''' --overwrite_output_dir to overcome.'''		)

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				)
							logger.info(
							    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1		)}, '
							    F'16-bits training: {training_args.fpaa}'		)
							logger.info(F'Training/evaluation parameters {training_args}'		)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							A      :					List[Any]      		=					AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,				cache_dir=model_args.cache_dir     ,				)

							A		,		A		,		A		,		A      :					Any      		=					get_tfds(
							    train_file=data_args.train_file     ,				eval_file=data_args.dev_file     ,				test_file=data_args.test_file     ,				tokenizer=lowerCamelCase_     ,				label_column_id=data_args.label_column_id     ,				max_seq_length=data_args.max_seq_length     ,				)

							A      :					str      		=					AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,				num_labels=len(lowerCamelCase_		)     ,				labelaid=lowerCamelCase_     ,				idalabel={id: label for label, id in labelaid.items()}     ,				finetuning_task='''text-classification'''     ,				cache_dir=model_args.cache_dir     ,				)

							with training_args.strategy.scope():
														A      :					Optional[Any]      		=					TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path     ,				from_pt=bool('''.bin''' in model_args.model_name_or_path		)     ,				config=lowerCamelCase_     ,				cache_dir=model_args.cache_dir     ,				)

							def compute_metrics(lowerCamelCase_		) -> Dict:
														A      :					Optional[int]      		=					np.argmax(p.predictions     ,				axis=1		)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							A      :					Optional[int]      		=					TFTrainer(
							    model=lowerCamelCase_     ,				args=lowerCamelCase_     ,				train_dataset=lowerCamelCase_     ,				eval_dataset=lowerCamelCase_     ,				compute_metrics=lowerCamelCase_     ,				)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir		)

							# Evaluation
							A      :					List[str]      		=					{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***'''		)
														A      :					str      		=					trainer.evaluate()
														A      :					Optional[int]      		=					os.path.join(training_args.output_dir     ,				'''eval_results.txt'''		)

														with open(lowerCamelCase_     ,				'''w'''		) as writer:
																					logger.info('''***** Eval results *****'''		)

																					for key, value in result.items():
																												logger.info(F'  {key} = {value}'		)
																												writer.write(F'{key} = {value}\n'		)

																					results.update(lowerCamelCase_		)

							return results


if __name__ == "__main__":
		main()









from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = "x"     ,				lowerCamelCase_ = 10**-10     ,				lowerCamelCase_ = 1     ,				):
							A      :					int      		=					symbols(lowerCamelCase_		)
							A      :					List[str]      		=					lambdify(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					lambdify(lowerCamelCase_     ,				diff(lowerCamelCase_     ,				lowerCamelCase_		)		)

							A      :					Optional[int]      		=					starting_point

							while True:
														if diff_function(lowerCamelCase_		) != 0:
																					A      :					Union[str, Any]      		=					prev_guess - multiplicity * func(lowerCamelCase_		) / diff_function(
																					    lowerCamelCase_		)
														else:
																					raise ZeroDivisionError('''Could not find root'''		) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess		) < precision:
																					return next_guess

														A      :					Tuple      		=					next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






